code: def forward(
      self,
      seq_group_meta_data_lists: List[SequenceGroupMetadata],
      # kv_cache in the format [[dict() for _ in range(2)] for _ in range(32)]
      kv_cache: Optional[List[List[Dict]]] = None,
      input_metadata: Optional[InputMetadata] = None,
  ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
      num_layers = self.model.config.num_hidden_layers
      # One for key, one for value
      decoder_kv_size = 2
  
      bigdl_input_ids = []
      bigdl_position_ids = []
      bigdl_attention_mask = []
  
      cur_seq_ids = []
      max_prompt_len = 0
  
      # 0. Verify is_prompt or is_decoding
      is_decoding_stage = not seq_group_meta_data_lists[0].is_prompt
  
      # 1. Assemble bigdl_input_ids
      for seq_group_meta_data in seq_group_meta_data_lists:
          # req_id = seq_group_meta_data.request_id
          # is_decoding_stage = is_decoding_stage and (not seq_group_meta_data.is_prompt)
          seq_ids = list(seq_group_meta_data.seq_data.keys())
          seq_id = seq_ids[0]
          cur_seq_ids.append(seq_id)
          seq_data = seq_group_meta_data.seq_data[seq_id]
  
          cur_seq_input_ids = seq_data.get_token_ids()
          # context_len = seq_data.get_len()
          if seq_group_meta_data.is_prompt:
              bigdl_input_ids.append(cur_seq_input_ids)
              max_prompt_len = max(max_prompt_len, seq_data.get_len())
          else:
              bigdl_input_ids.append([cur_seq_input_ids[-1]])
      # 1. Assemble bigdl_input_ids end
  
      if is_decoding_stage:
          bigdl_kv_cache = self.prepare_kv_cache(
              cur_seq_ids,
              seq_group_meta_data_lists,
              kv_cache,
              num_layers,
              decoder_kv_size,
          )
      else:
          bigdl_attention_mask = _get_attention_mask_for_prompts(
              bigdl_input_ids, max_prompt_len
          )
          bigdl_input_ids = [
              _pad_to_max(input_ids, max_prompt_len, self.pad_token_id)
              for input_ids in bigdl_input_ids
          ]
  
      if is_decoding_stage:
          cur_seq_len = bigdl_kv_cache[0][0].size(2)
          for seq_group_meta_data in seq_group_meta_data_lists:
              seq_ids = list(seq_group_meta_data.seq_data.keys())
              seq_id = seq_ids[0]
              seq_data = seq_group_meta_data.seq_data[seq_id]
              cur_pos = seq_data.get_len()
              # bigdl_position_ids.append([cur_pos - 1])
              cur_attention_mask = [0] * (cur_seq_len - cur_pos + 1) + [
                  1
              ] * (cur_pos)
              bigdl_attention_mask.append(cur_attention_mask)
  
      bigdl_input_ids = torch.tensor(
          bigdl_input_ids, device=self.device
      )
  
      if is_decoding_stage:
          # bigdl_position_ids = torch.tensor(bigdl_position_ids, device=self.device)
          bigdl_attention_mask = torch.tensor(
              bigdl_attention_mask, device=self.device
          )
          kwargs = {
              "input_ids": bigdl_input_ids,
              # "position_ids": bigdl_position_ids,
              "attention_mask": bigdl_attention_mask,
              "past_key_values": bigdl_kv_cache,
              "use_cache": True,
              # "return_dict": True,
          }
      else:
          kwargs = {
              "input_ids": bigdl_input_ids,
              "attention_mask": torch.tensor(
                  bigdl_attention_mask, device=self.device
              ),
              # "position_ids": bigdl_position_ids,
              "past_key_values": None,
              "use_cache": True,
              # "return_dict": True,
          }
          if self.last_kv_cache:
              self.last_kv_cache = None
      # pdb.set_trace()
  
      if self.device.type == "xpu":
          torch.xpu.empty_cache()
      st_timestamp = time.perf_counter()
      outputs = self.model.forward(**kwargs)
      # tmp = torch.xpu.memory_stats()
      # logger.info(f"0: {tmp['allocated_bytes.all.current']}")
      # self.last_seq_ids = cur_seq_ids[:]
      # self.last_kv_cache = outputs.past_key_values
      self._set_last_seq_ids(cur_seq_ids[:])
      self._set_last_kv_cache(outputs.past_key_values)
      # pdb.set_trace()
  
      logits = outputs.logits[:, -1, :]
      bigdl_output = self.sampler(logits, input_metadata, st_timestamp)
      # tmp = torch.xpu.memory_stats()
      # logger.info(f"before: {tmp['allocated_bytes.all.current']}")
  
      self.update_kv_cache(
          cur_seq_ids, kv_cache, num_layers, decoder_kv_size
      )
  
      # tmp = torch.xpu.memory_stats()
      # logger.info(f"after: {tmp['allocated_bytes.all.current']}")
      return bigdl_output
  
file_path: BigDL/python/llm/src/bigdl/llm/vllm/model_executor/models/bigdl_mixtral.py
imports: import torch
  from torch import nn
  from transformers import AutoTokenizer, PreTrainedTokenizerBase, LlamaConfig
  from typing import Optional, Tuple, List, Type, Dict
  from bigdl.llm.vllm.sequence import SequenceOutputs, SequenceGroupMetadata
  from bigdl.llm.vllm.model_executor.layers.bigdl_sampler import BigDLSampler
  from bigdl.llm.vllm.model_executor.models.bigdl_model import BigDLModelForCausalLM
  from bigdl.llm.vllm.logger import init_logger
  import math
  import time
  from bigdl.llm.vllm.model_executor.input_metadata import InputMetadata
  from transformers.generation.logits_process import LogitsProcessorList, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper
label_desc: Use image node_id 2 for calling the BigDLMixtralForCausalLM obj's underlying member method code with example usage: obj.forward(seq_group_meta_data_lists, kv_cache, input_metadata) and returns: bigdl_output